Geospatial Data Science - Final project
Analysis of US road accidents and construction sites
Moritz Peist
Noemi Lucchi
Simon Vellin
Load packages
library(data.table) # For faster data manipulation
library(tidyverse) # For data manipulation and visualization## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::between() masks data.table::between()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks data.table::first()
## ✖ lubridate::hour() masks data.table::hour()
## ✖ lubridate::isoweek() masks data.table::isoweek()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::last() masks data.table::last()
## ✖ lubridate::mday() masks data.table::mday()
## ✖ lubridate::minute() masks data.table::minute()
## ✖ lubridate::month() masks data.table::month()
## ✖ lubridate::quarter() masks data.table::quarter()
## ✖ lubridate::second() masks data.table::second()
## ✖ purrr::transpose() masks data.table::transpose()
## ✖ lubridate::wday() masks data.table::wday()
## ✖ lubridate::week() masks data.table::week()
## ✖ lubridate::yday() masks data.table::yday()
## ✖ lubridate::year() masks data.table::year()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(sf) # For spatial data handling## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUElibrary(leaflet) # For interactive maps
library(leaflet.extras) # For additional leaflet features
library(mapview) # For easier map visualization
library(tmap) # For thematic maps
library(tigris) # For US road networks## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.library(future) # For parallel processing
library(future.apply) # For parallel processing with apply functions
library(sf) # For spatial data handling
# Remove PostgreSQL packages as we're not using them anymore
# library(RPostgres) # For PostgreSQL connection
# library(DBI) # For database interface
# Create directories if they don't exist
if (!dir.exists("./data/tigris")) {
dir.create("./data/tigris", recursive = TRUE)
}
# Set custom cache directory (optional)
options(tigris_cache_dir = "./data/tigris")
# Configure tigris to use caching
options(tigris_use_cache = TRUE)Set-up convenience functions for parallel processing (as data is quite large)
# Function to create buffers in batches with proper projection
create_buffers_in_batches <- function(sf_object, buffer_dist, batch_size = 500) {
# First, reproject to an appropriate projected CRS for the region
# For California, UTM Zone 10N (EPSG:26910) or 11N (EPSG:26911) works well
# If we're unsure about your region, a web mercator projection (3857) is a reasonable default
message("Reprojecting data to a meter-based CRS...")
sf_object_projected <- st_transform(sf_object, 3310) # California Albers
n_features <- nrow(sf_object_projected)
n_batches <- ceiling(n_features / batch_size)
# Create empty list to store results
buffer_list <- vector("list", n_batches)
# Process in batches
for (i in 1:n_batches) {
start_idx <- (i-1) * batch_size + 1
end_idx <- min(i * batch_size, n_features)
cat(sprintf("Processing batch %d of %d (features %d to %d)\n",
i, n_batches, start_idx, end_idx))
# Extract batch
batch <- sf_object_projected[start_idx:end_idx, ]
# Create buffer (with parallel processing within sf)
buffer_list[[i]] <- st_buffer(batch, dist = buffer_dist)
}
# Combine results
result <- do.call(rbind, buffer_list)
# Reproject back to original CRS if needed
message("Reprojecting results back to original CRS...")
st_transform(result, st_crs(sf_object))
}Load data (filter for California for now to limit data set size)
# Efficient approach
df.acc <- fread("data/us_accidents/US_accidents_March23.csv")[
# Filter date range of 2021
lubridate::year(as.Date(Start_Time)) == 2021 &
# And California
State == "CA"
][, `:=`(
# Add year, quarter, month columns
year = data.table::year(Start_Time),
quarter = data.table::quarter(Start_Time),
month = data.table::month(Start_Time),
# Calculate duration (assuming End_Time exists in the dataset)
duration = as.numeric(difftime(End_Time, Start_Time, units = "days"))
)] %>%
as_tibble() # Convert to tibble only at the end for performancedf.const <- fread("data/us_constructions/US_constructions_Dec21.csv")[
# Filter date range of 2021
lubridate::year(as.Date(Start_Time)) == 2021 &
# And California
State == "CA"
][, `:=`(
# Add year, quarter, month columns
year = year(Start_Time),
quarter = quarter(Start_Time),
month = month(Start_Time),
# Calculate duration (assuming End_Time exists in the dataset)
duration = as.numeric(difftime(End_Time, Start_Time, units = "days"))
)] %>%
as_tibble() # Convert to tibble only at the end for performanceConvert dataframes to sf objects
# Convert accident data to sf object
df.acc.sf <- df.acc %>%
filter(!is.na(Start_Lat) & !is.na(Start_Lng)) %>%
st_as_sf(coords = c("Start_Lng", "Start_Lat"), crs = 4326)
# Convert construction data to sf object
df.const.sf <- df.const %>%
filter(!is.na(Start_Lat) & !is.na(Start_Lng)) %>%
st_as_sf(coords = c("Start_Lng", "Start_Lat"), crs = 4326)# Get US roads (this can be slow for the entire US, so we might want to limit by state)
# We also only get Primary and Secondary roads as they are the most important (i.e., where most constructions and accidents happen). Otherwise, the dataset would become to large (1.1GB)
ca_roads <- primary_secondary_roads("CA", year = 2021)
# Below code to get ALL roads for California (not recommended due to size)
# First get all counties in California
#ca_counties <- counties("CA", year = 2021)
# Then get roads for each county
#ca_roads_list <- lapply(ca_counties$COUNTYFP, function(county_code) {
# roads("CA", county = county_code, year = 2021)
#})
# Optionally combine all county road data into one object
#ca_roads <- do.call(rbind, ca_roads_list)# Static map for Accidents with year coloring
# First, store your plot in a variable
ca_plot <- ggplot() +
geom_sf(data = ca_roads, color = "gray80", size = 0.1) +
geom_sf(data = df.acc.sf %>% filter(State == "CA"),
aes(color = factor(year)), alpha = 0.7, size = 0.5) +
scale_color_viridis_d(name = "Year") + # Use viridis color palette for discrete values
theme_minimal() +
labs(title = "US Road Accidents by Year (2016-2021)") +
theme(legend.position = "bottom")
print(ca_plot)
# Then save it using ggsave
ggsave(filename = "imgs/california_accidents.png",
plot = ca_plot,
width = 10, # width in inches
height = 8, # height in inches
dpi = 300) # resolution
# Construction sites map with year coloring
# First, store our plot in a variable
ca_const_plot <- ggplot() +
geom_sf(data = ca_roads, color = "gray80", size = 0.1) +
geom_sf(data = df.const.sf %>% filter(State == "CA"),
aes(color = factor(year)), alpha = 0.7, size = 0.5) +
scale_color_viridis_d(name = "Year") + # Use viridis color palette for discrete values
theme_minimal() +
labs(title = "US Construction Sites by Year (2016-2021)") +
theme(legend.position = "bottom")
print(ca_const_plot)
# Then save it using ggsave
ggsave(filename = "imgs/california_construction.png",
plot = ca_const_plot,
width = 10, # width in inches
height = 8, # height in inches
dpi = 300) # resolution# Create heatmap of accidents using ggplot2
# Modified accident heatmap with transparency for low density areas
accident_heatmap <- ggplot() +
# Add California roads as base layer
geom_sf(data = ca_roads, color = "gray80", size = 0.1) +
# Create density heatmap with transparency threshold
stat_density_2d(
data = df.acc %>% filter(!is.na(Start_Lat) & !is.na(Start_Lng)),
aes(x = Start_Lng, y = Start_Lat, fill = after_stat(density), alpha = after_stat(density)),
geom = "tile",
contour = FALSE,
h = 0.1,
n = 200
) +
# Set alpha scaling with a minimum threshold
scale_alpha_continuous(range = c(0, 0.9), guide = "none") +
# Use color scheme similar to leaflet's default heatmap
scale_fill_gradientn(
colors = c("#0000FF", "#00FFFF", "#00FF00", "#FFFF00", "#FF0000"),
name = "Accident\nDensity"
) +
coord_sf(
xlim = c(-124.5, -114.5),
ylim = c(32.5, 42.5)
) +
theme_minimal() +
labs(
title = "Accident Density Heatmap in California (2016-2021)",
x = NULL,
y = NULL
) +
theme(
legend.position = "right",
plot.title = element_text(face = "bold")
)
# Display the plot
print(accident_heatmap)
ggsave(filename = "imgs/accident_heatmap.png",
plot = accident_heatmap,
width = 10, # width in inches
height = 8, # height in inches
dpi = 300) # resolution# Create heatmap of construction sites using ggplot2
construction_heatmap <- ggplot() +
# Add California roads as base layer
geom_sf(data = ca_roads, color = "gray80", size = 0.1) +
# Create density heatmap
stat_density_2d(
data = df.const %>% filter(!is.na(Start_Lat) & !is.na(Start_Lng)),
aes(x = Start_Lng, y = Start_Lat, fill = after_stat(density)),
geom = "tile",
contour = FALSE,
alpha = 0.7,
h = 0.1, # Bandwidth
n = 200 # Resolution
) +
# Set alpha scaling with a minimum threshold
scale_alpha_continuous(range = c(0, 0.9), guide = "none") +
# Use color scheme matching your leaflet construction map
scale_fill_gradientn(
colors = c("yellow", "orange", "red"),
name = "Construction\nDensity"
) +
# Set appropriate boundaries for California
coord_sf(
xlim = c(-124.5, -114.5),
ylim = c(32.5, 42.5)
) +
theme_minimal() +
labs(
title = "Construction Density Heatmap in California (2016-2021)",
x = NULL,
y = NULL
) +
theme(
legend.position = "right",
plot.title = element_text(face = "bold")
)
# Display the plot
print(construction_heatmap)
Heatmaps - interactive
# For interactive maps (often better for large datasets)
# Accidents map
accident_map <- leaflet() %>%
addTiles() %>%
addHeatmap(data = df.acc.sf,
intensity = ~1,
radius = 8,
blur = 10) %>%
setView(lng = -119.4179, lat = 36.7783, zoom = 6) %>% # Center to CA
setMaxBounds(lng1 = -124.6, lat1 = 42.0, # Northwest corner of CA
lng2 = -114.1, lat2 = 32.5) # Southeast corner of CA
# Display maps
accident_map# Construction sites map with California focus and bounds constraints
construction_map <- leaflet() %>%
addTiles() %>%
addHeatmap(data = df.const.sf,
intensity = ~1,
radius = 8,
blur = 10,
gradient = c("yellow", "orange", "red")) %>%
setView(lng = -119.4179, lat = 36.7783, zoom = 6) %>% # Center on California
setMaxBounds(lng1 = -124.6, lat1 = 42.0, # Northwest corner of CA
lng2 = -114.1, lat2 = 32.5) # Southeast corner of CA
# Display the map
construction_mapWe construct construction sites now also as line objects since they have a start and end point
# Convert construction data to linestring sf object
df.const.lines <- df.const %>%
filter(!is.na(Start_Lat) & !is.na(Start_Lng) & !is.na(End_Lat) & !is.na(End_Lng)) %>%
mutate(geometry = pmap(list(Start_Lng, Start_Lat, End_Lng, End_Lat),
function(start_lng, start_lat, end_lng, end_lat) {
coords <- matrix(c(start_lng, start_lat,
end_lng, end_lat),
ncol = 2, byrow = TRUE)
st_linestring(coords)
})) %>%
st_sf(crs = 4326)Spatial operations in R (replacing PostGIS)
# Set up parallel processing to speed up computations
future::plan(future::multisession, workers = parallel::detectCores() - 1)
cat("Creating construction buffers...\n")## Creating construction buffers...# Step 1A: Create construction point buffers
# Transform to meter-based CRS, buffer, and transform back
construction_buffers <- df.const.sf %>%
# Add ID as a primary key if it doesn't exist
mutate(construction_id = if("ID" %in% names(.)) ID else row_number()) %>%
# Select only needed columns
select(construction_id, Start_Time, End_Time, geometry) %>%
# Rename columns to match our schema
rename(start_time = Start_Time, end_time = End_Time) %>%
# Create the buffer using our batch function
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## Processing batch 407 of 407 (features 203001 to 203015)## Reprojecting results back to original CRS...cat("Creating construction line buffers...\n")## Creating construction line buffers...# Calculate segment length in meters
df.const.lines_with_length <- df.const.lines %>%
# Transform to meter-based CRS
st_transform(3310) %>%
# Calculate length in meters
mutate(segment_length_m = as.numeric(st_length(geometry))) %>%
# Transform back to WGS84
st_transform(4326)
# Step 1A-L: Create construction line buffers
construction_line_buffers <- df.const.lines_with_length %>%
# Add ID as a primary key if it doesn't exist
mutate(construction_id = if("ID" %in% names(.)) ID else row_number()) %>%
# Select only needed columns
select(construction_id, Start_Time, End_Time, segment_length_m, geometry) %>%
# Rename columns to match our schema
rename(start_time = Start_Time, end_time = End_Time) %>%
# Create the buffer using our batch function
create_buffers_in_batches(buffer_dist = 300, batch_size = 250)## Reprojecting data to a meter-based CRS...## Processing batch 1 of 693 (features 1 to 250)
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## Processing batch 693 of 693 (features 173001 to 173023)## Reprojecting results back to original CRS...# Step 1B: Spatial join to find accidents within construction buffers
cat("Finding accidents within construction buffers...\n")## Finding accidents within construction buffers...spatial_matches <- df.acc.sf %>%
# Prepare accident data
mutate(accident_id = if("ID" %in% names(.)) ID else row_number(),
accident_severity = if("Severity" %in% names(.)) Severity else NA_integer_,
accident_time = Start_Time) %>%
# Select only needed columns
select(accident_id, accident_severity, accident_time, geometry) %>%
# Spatial join with construction buffers
st_join(construction_buffers, join = st_within) %>%
# Filter for accidents within temporal range of construction
filter(!is.na(construction_id) &
accident_time >= start_time &
accident_time <= end_time) %>%
# Select final columns
select(accident_id, accident_severity, accident_time, construction_id)
# Step 1B-L: Spatial join to find accidents within construction line buffers
cat("Finding accidents within construction line buffers...\n")## Finding accidents within construction line buffers...line_spatial_matches <- df.acc.sf %>%
# Prepare accident data
mutate(accident_id = if("ID" %in% names(.)) ID else row_number(),
accident_severity = if("Severity" %in% names(.)) Severity else NA_integer_,
accident_time = Start_Time) %>%
# Select only needed columns
select(accident_id, accident_severity, accident_time, geometry) %>%
# Spatial join with construction line buffers
st_join(construction_line_buffers, join = st_within) %>%
# Filter for accidents within temporal range of construction
filter(!is.na(construction_id) &
accident_time >= start_time &
accident_time <= end_time) %>%
# Select final columns
select(accident_id, accident_severity, accident_time, construction_id, segment_length_m)
# Step 2: Count construction sites per accident
cat("Counting construction sites per accident...\n")## Counting construction sites per accident...construction_counts <- spatial_matches %>%
# Convert to regular dataframe (don't need geometry anymore)
st_set_geometry(NULL) %>%
# Group and count
group_by(accident_id) %>%
summarize(nearby_construction_count = n())
# Step 2-L: Count construction line sites per accident
cat("Counting construction line sites per accident...\n")## Counting construction line sites per accident...line_construction_counts <- line_spatial_matches %>%
# Convert to regular dataframe (don't need geometry anymore)
st_set_geometry(NULL) %>%
# Group and count
group_by(accident_id) %>%
summarize(nearby_line_construction_count = n())
# Step 3: Final analysis table - one construction site per accident
cat("Creating final analysis tables...\n")## Creating final analysis tables...# Create a function to randomly select one construction site per accident
select_random_construction <- function(df) {
df %>%
group_by(accident_id) %>%
slice_sample(n = 1) %>%
ungroup()
}
# Apply for point construction sites
accident_construction_analysis <- spatial_matches %>%
st_set_geometry(NULL) %>%
# Join with counts
left_join(construction_counts, by = "accident_id") %>%
# Add flags
mutate(
construction_type = 'POINT',
temporal_overlap = 1,
is_near_active_construction = 1
) %>%
# Select one construction site per accident randomly
select_random_construction()
# Apply for line construction sites
line_accident_construction_analysis <- line_spatial_matches %>%
st_set_geometry(NULL) %>%
# Join with counts
left_join(line_construction_counts, by = "accident_id") %>%
# Add flags
mutate(
temporal_overlap = 1,
is_near_active_construction = 1
) %>%
# Select one construction site per accident randomly
select_random_construction()
# Step 4-L: Calculate accident rates by segment length
cat("Calculating accident rates by segment length...\n")## Calculating accident rates by segment length...construction_segment_accident_rates <- df.const.lines_with_length %>%
# Ensure we have an ID column
mutate(construction_id = if("ID" %in% names(.)) ID else row_number()) %>%
# Left join with accident analysis
left_join(
line_accident_construction_analysis %>%
group_by(construction_id) %>%
summarize(accident_count = n()),
by = "construction_id"
) %>%
# Replace NA counts with 0
mutate(accident_count = replace_na(accident_count, 0)) %>%
# Calculate accidents per km
mutate(accidents_per_km = accident_count / (segment_length_m / 1000))
# Return to sequential processing
future::plan(future::sequential)
# Total accidents summary - for comparison
total_accidents <- nrow(df.acc.sf)
accidents_near_construction <- nrow(accident_construction_analysis)
cat("Total accidents in dataset:", total_accidents, "\n")## Total accidents in dataset: 341876cat("Accidents near active construction:", accidents_near_construction, "\n")## Accidents near active construction: 59182cat("Percentage of accidents near active construction: ",
round(accidents_near_construction/total_accidents*100, 2), "%\n")## Percentage of accidents near active construction: 17.31 %# Get statistics on construction zones
accidents_per_zone <- construction_segment_accident_rates %>%
filter(accident_count > 0) %>%
select(construction_id, accident_count, geometry)
cat("Construction zones with accidents:", nrow(accidents_per_zone), "\n")## Construction zones with accidents: 12259cat("Maximum accidents in a single construction zone:", max(accidents_per_zone$accident_count), "\n")## Maximum accidents in a single construction zone: 695cat("Average accidents per construction zone:", round(mean(accidents_per_zone$accident_count), 2), "\n")## Average accidents per construction zone: 8.25# Join accident data with construction info
accidents_with_construction <- df.acc.sf %>%
mutate(accident_id = if("ID" %in% names(.)) ID else row_number()) %>%
left_join(
accident_construction_analysis %>%
select(accident_id, construction_id, nearby_construction_count, construction_type),
by = "accident_id"
) %>%
mutate(near_construction = ifelse(is.na(construction_id), 0, 1))
# Construction density statistics
construction_density_stats <- accident_construction_analysis %>%
group_by(nearby_construction_count) %>%
summarize(accident_count = n()) %>%
arrange(nearby_construction_count)
print(construction_density_stats)## # A tibble: 93 × 2
## nearby_construction_count accident_count
## <int> <int>
## 1 1 32123
## 2 2 11572
## 3 3 5336
## 4 4 2774
## 5 5 1820
## 6 6 1117
## 7 7 794
## 8 8 540
## 9 9 492
## 10 10 327
## # ℹ 83 more rows# Severity analysis near line construction
severity_by_construction <- line_accident_construction_analysis %>%
group_by(accident_severity) %>%
summarize(count = n()) %>%
arrange(accident_severity)
# Comparison of accident severity near vs. away from construction
accidents_near <- accidents_with_construction %>%
filter(near_construction == 1) %>%
st_set_geometry(NULL) %>%
mutate(location = "Near construction") %>%
select(Severity, location)
accidents_away <- accidents_with_construction %>%
filter(near_construction == 0) %>%
st_set_geometry(NULL) %>%
mutate(location = "Away from construction") %>%
select(Severity, location)
severity_comparison <- bind_rows(accidents_near, accidents_away) %>%
group_by(Severity, location) %>%
summarize(count = n(), .groups = "drop") %>%
arrange(Severity, location)
# Time-based analysis
temporal_analysis <- line_accident_construction_analysis %>%
mutate(month = floor_date(accident_time, "month")) %>%
group_by(month) %>%
summarize(accident_count = n()) %>%
arrange(month)Buffer zone analysis
The code below implements the PostGIS functionality directly in R.
# Visualize construction zones with accident counts
# First, store our plot in a variable
accident_zones_plot <- ggplot() +
geom_sf(data = ca_roads, color = "gray80", size = 0.1) +
# Arrange data in descending order so high frequency zones are plotted on top
geom_sf(data = accidents_per_zone %>% arrange(accident_count),
aes(color = accident_count), size = 1.5) +
scale_color_viridis_c() +
theme_minimal() +
labs(title = "Accident Counts within Construction Zones (2021)")
print(accident_zones_plot)
# Then save it using ggsave
ggsave(filename = "imgs/accident_counts_construction_zones.png",
plot = accident_zones_plot,
width = 10, # width in inches
height = 8, # height in inches
dpi = 300) # resolutionAdvanced construction sit analysis (line objects):
# Static map with construction line segments
ggplot() +
geom_sf(data = ca_roads, color = "gray80", size = 0.1) +
geom_sf(data = df.const.lines %>% filter(State == "CA"),
color = "orange", size = 1) +
theme_minimal() +
labs(title = "US Construction Segments (2021)")
# Interactive map with construction segments focused on California
construction_segment_map <- leaflet() %>%
addTiles() %>%
addPolylines(data = df.const.lines,
color = "orange",
weight = 3,
opacity = 0.7,
popup = ~paste("ID:", ID, "<br>Duration:", duration)) %>%
setView(lng = -119.4179, lat = 36.7783, zoom = 6) %>% # Center on California
setMaxBounds(lng1 = -124.6, lat1 = 42.0, # Northwest corner of CA
lng2 = -114.1, lat2 = 32.5) # Southeast corner of CA
# Display the map
construction_segment_map